1. Field of the Invention
Embodiments of the present invention generally relate detecting double talk in acoustic echo cancellation using zero-crossing rate.
2. Description of the Related Art
The history of acoustic echo cancellation (AEC) dates back to 1960s. As discussed in S. Gay and J. Benesty, Eds., “Acoustic Signal Processing for Telecommunication,” pp. 2-13, Kluwer Academic, Norwell, Mass., 2000, [Gay herein], a number of attractive techniques have been proposed to address various aspects of AEC. One of the primary issues in the forefront of AEC development is handling of double talk (DT). DT occurs when both near-end and far-end speakers talk at the same time. The adaptive filter in an AEC is typically designed to cancel only the far-end echo, and the presence of a near-end signal strongly influences the convergence of the filter. The DT may cause divergence of the adaptive filter, thus causing the far-end listener to hear an echo, which is annoying and undesirable. Thus, it is important to detect DT and control its impact as quickly as possible.
Several techniques have been proposed to detect DT and thus avoid filter divergence. DT detectors are an integral part of almost all commercially available echo cancellers. A review of classical DT detection methods can be found in T. Gänsler, et al., “Double-Talk Robust Fast Converging Algorithms for Network Echo Cancellation,” IEEE Transactions on Speech and Audio Processing, Vol. 8, No. 6, pp. 656-663, November, 2000. Some techniques control DT by restricting the communication to only one way, i.e., half-duplex, which may not be suitable for many applications. In other techniques, a DT detector is used that freezes filter adaptation in the presence of DT. To benefit from frozen filter adaptation, the DT detector needs to correctly estimate the start and end of the near-end speech. Any misdetection may lead to echo leakage to the far end. Also, the echo cancellation may suffer if the echo path changes during the time the filter adaptation is frozen.
The detection of DT is challenging as known techniques require a large amount of data for computation of correlation measures to detect DT. Furthermore, to help ensure that DT is detected, the measures are computed for each audio sample. The detection of DT may, therefore, be too computationally expensive for implementation on low-resource processors executing AEC.